{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "kJ8DaFDGn2X0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris = datasets.load_iris()"
      ],
      "metadata": {
        "id": "BGwd30jCoPGp"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "id": "5G1S1dHmoRVE"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "INY7oM3boUeC"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "EulaEQQgoXfz"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_encoded = to_categorical(y_train)\n",
        "y_test_encoded = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "_bVdsJmXoZ7w"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    Dense(64, activation='relu', input_shape=(4,)),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dense(3, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "g18eEreBocSx"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "1lb9Jz_pofs9"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(X_train_scaled, y_train_encoded, epochs=100, batch_size=4, validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6lWGR05okUt",
        "outputId": "b47f9d89-d687-44cb-ffdc-9a9d0926d7f9"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "27/27 [==============================] - 1s 14ms/step - loss: 0.9356 - accuracy: 0.5926 - val_loss: 0.8169 - val_accuracy: 0.9167\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.5941 - accuracy: 0.7963 - val_loss: 0.6571 - val_accuracy: 0.8333\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.4474 - accuracy: 0.8148 - val_loss: 0.5371 - val_accuracy: 0.8333\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.3711 - accuracy: 0.8333 - val_loss: 0.4564 - val_accuracy: 0.9167\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.3255 - accuracy: 0.8241 - val_loss: 0.4112 - val_accuracy: 0.9167\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.2865 - accuracy: 0.8704 - val_loss: 0.3727 - val_accuracy: 0.9167\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.2577 - accuracy: 0.8889 - val_loss: 0.3395 - val_accuracy: 0.9167\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.2252 - accuracy: 0.9444 - val_loss: 0.3082 - val_accuracy: 0.9167\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.2052 - accuracy: 0.9074 - val_loss: 0.3089 - val_accuracy: 0.9167\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.1821 - accuracy: 0.9444 - val_loss: 0.3148 - val_accuracy: 0.9167\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.1600 - accuracy: 0.9537 - val_loss: 0.2952 - val_accuracy: 0.9167\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.1467 - accuracy: 0.9630 - val_loss: 0.2702 - val_accuracy: 0.9167\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.1322 - accuracy: 0.9722 - val_loss: 0.3056 - val_accuracy: 0.9167\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.1185 - accuracy: 0.9352 - val_loss: 0.2758 - val_accuracy: 0.9167\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.1132 - accuracy: 0.9630 - val_loss: 0.3377 - val_accuracy: 0.9167\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.1041 - accuracy: 0.9444 - val_loss: 0.3310 - val_accuracy: 0.9167\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0975 - accuracy: 0.9630 - val_loss: 0.3091 - val_accuracy: 0.9167\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.1006 - accuracy: 0.9722 - val_loss: 0.3301 - val_accuracy: 0.9167\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0840 - accuracy: 0.9537 - val_loss: 0.3387 - val_accuracy: 0.9167\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0821 - accuracy: 0.9722 - val_loss: 0.3769 - val_accuracy: 0.9167\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0748 - accuracy: 0.9722 - val_loss: 0.3554 - val_accuracy: 0.9167\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0743 - accuracy: 0.9722 - val_loss: 0.3966 - val_accuracy: 0.9167\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0709 - accuracy: 0.9722 - val_loss: 0.3771 - val_accuracy: 0.9167\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0700 - accuracy: 0.9722 - val_loss: 0.3954 - val_accuracy: 0.9167\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0648 - accuracy: 0.9815 - val_loss: 0.4117 - val_accuracy: 0.9167\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0677 - accuracy: 0.9815 - val_loss: 0.3662 - val_accuracy: 0.9167\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0716 - accuracy: 0.9630 - val_loss: 0.4129 - val_accuracy: 0.9167\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0877 - accuracy: 0.9444 - val_loss: 0.3819 - val_accuracy: 0.9167\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0655 - accuracy: 0.9722 - val_loss: 0.4655 - val_accuracy: 0.9167\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0607 - accuracy: 0.9815 - val_loss: 0.3738 - val_accuracy: 0.9167\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0585 - accuracy: 0.9722 - val_loss: 0.4470 - val_accuracy: 0.9167\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0600 - accuracy: 0.9722 - val_loss: 0.4259 - val_accuracy: 0.9167\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0550 - accuracy: 0.9815 - val_loss: 0.4348 - val_accuracy: 0.9167\n",
            "Epoch 34/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0574 - accuracy: 0.9815 - val_loss: 0.4250 - val_accuracy: 0.9167\n",
            "Epoch 35/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0617 - accuracy: 0.9722 - val_loss: 0.4848 - val_accuracy: 0.9167\n",
            "Epoch 36/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0688 - accuracy: 0.9722 - val_loss: 0.4467 - val_accuracy: 0.9167\n",
            "Epoch 37/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0644 - accuracy: 0.9630 - val_loss: 0.4273 - val_accuracy: 0.9167\n",
            "Epoch 38/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0552 - accuracy: 0.9815 - val_loss: 0.4687 - val_accuracy: 0.9167\n",
            "Epoch 39/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0542 - accuracy: 0.9815 - val_loss: 0.5133 - val_accuracy: 0.9167\n",
            "Epoch 40/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0515 - accuracy: 0.9815 - val_loss: 0.4815 - val_accuracy: 0.9167\n",
            "Epoch 41/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0540 - accuracy: 0.9815 - val_loss: 0.5322 - val_accuracy: 0.9167\n",
            "Epoch 42/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0492 - accuracy: 0.9630 - val_loss: 0.5117 - val_accuracy: 0.9167\n",
            "Epoch 43/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0461 - accuracy: 0.9815 - val_loss: 0.5108 - val_accuracy: 0.9167\n",
            "Epoch 44/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0486 - accuracy: 0.9815 - val_loss: 0.5193 - val_accuracy: 0.9167\n",
            "Epoch 45/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0520 - accuracy: 0.9722 - val_loss: 0.4719 - val_accuracy: 0.9167\n",
            "Epoch 46/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0558 - accuracy: 0.9722 - val_loss: 0.5531 - val_accuracy: 0.9167\n",
            "Epoch 47/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0495 - accuracy: 0.9815 - val_loss: 0.5279 - val_accuracy: 0.9167\n",
            "Epoch 48/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0455 - accuracy: 0.9815 - val_loss: 0.5324 - val_accuracy: 0.9167\n",
            "Epoch 49/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0484 - accuracy: 0.9815 - val_loss: 0.5500 - val_accuracy: 0.9167\n",
            "Epoch 50/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0495 - accuracy: 0.9815 - val_loss: 0.5321 - val_accuracy: 0.9167\n",
            "Epoch 51/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0443 - accuracy: 0.9907 - val_loss: 0.5456 - val_accuracy: 0.9167\n",
            "Epoch 52/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0437 - accuracy: 0.9815 - val_loss: 0.5484 - val_accuracy: 0.9167\n",
            "Epoch 53/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0516 - accuracy: 0.9722 - val_loss: 0.5497 - val_accuracy: 0.9167\n",
            "Epoch 54/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0434 - accuracy: 0.9815 - val_loss: 0.5500 - val_accuracy: 0.9167\n",
            "Epoch 55/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0405 - accuracy: 0.9907 - val_loss: 0.5638 - val_accuracy: 0.9167\n",
            "Epoch 56/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0433 - accuracy: 0.9815 - val_loss: 0.6283 - val_accuracy: 0.9167\n",
            "Epoch 57/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0460 - accuracy: 0.9815 - val_loss: 0.5503 - val_accuracy: 0.9167\n",
            "Epoch 58/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.5868 - val_accuracy: 0.9167\n",
            "Epoch 59/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0442 - accuracy: 0.9722 - val_loss: 0.5711 - val_accuracy: 0.9167\n",
            "Epoch 60/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0461 - accuracy: 0.9630 - val_loss: 0.5685 - val_accuracy: 0.9167\n",
            "Epoch 61/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0440 - accuracy: 0.9907 - val_loss: 0.5941 - val_accuracy: 0.9167\n",
            "Epoch 62/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0365 - accuracy: 0.9907 - val_loss: 0.5910 - val_accuracy: 0.9167\n",
            "Epoch 63/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0365 - accuracy: 0.9907 - val_loss: 0.6173 - val_accuracy: 0.9167\n",
            "Epoch 64/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0371 - accuracy: 0.9907 - val_loss: 0.5918 - val_accuracy: 0.9167\n",
            "Epoch 65/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0345 - accuracy: 0.9907 - val_loss: 0.6566 - val_accuracy: 0.9167\n",
            "Epoch 66/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0386 - accuracy: 0.9815 - val_loss: 0.6215 - val_accuracy: 0.9167\n",
            "Epoch 67/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0357 - accuracy: 0.9907 - val_loss: 0.5990 - val_accuracy: 0.9167\n",
            "Epoch 68/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.5837 - val_accuracy: 0.9167\n",
            "Epoch 69/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0360 - accuracy: 0.9907 - val_loss: 0.6601 - val_accuracy: 0.9167\n",
            "Epoch 70/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0365 - accuracy: 0.9815 - val_loss: 0.6288 - val_accuracy: 0.9167\n",
            "Epoch 71/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0373 - accuracy: 0.9815 - val_loss: 0.6209 - val_accuracy: 0.9167\n",
            "Epoch 72/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0323 - accuracy: 0.9815 - val_loss: 0.6582 - val_accuracy: 0.9167\n",
            "Epoch 73/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0347 - accuracy: 0.9907 - val_loss: 0.6782 - val_accuracy: 0.9167\n",
            "Epoch 74/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0363 - accuracy: 0.9815 - val_loss: 0.6135 - val_accuracy: 0.9167\n",
            "Epoch 75/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.6428 - val_accuracy: 0.9167\n",
            "Epoch 76/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0316 - accuracy: 0.9907 - val_loss: 0.6552 - val_accuracy: 0.9167\n",
            "Epoch 77/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0365 - accuracy: 0.9907 - val_loss: 0.6731 - val_accuracy: 0.9167\n",
            "Epoch 78/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0406 - accuracy: 0.9815 - val_loss: 0.6498 - val_accuracy: 0.9167\n",
            "Epoch 79/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0308 - accuracy: 0.9907 - val_loss: 0.6833 - val_accuracy: 0.9167\n",
            "Epoch 80/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0300 - accuracy: 0.9907 - val_loss: 0.6833 - val_accuracy: 0.9167\n",
            "Epoch 81/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0352 - accuracy: 0.9815 - val_loss: 0.6527 - val_accuracy: 0.9167\n",
            "Epoch 82/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0307 - accuracy: 0.9907 - val_loss: 0.6864 - val_accuracy: 0.9167\n",
            "Epoch 83/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0318 - accuracy: 0.9907 - val_loss: 0.7008 - val_accuracy: 0.9167\n",
            "Epoch 84/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0302 - accuracy: 0.9815 - val_loss: 0.7250 - val_accuracy: 0.9167\n",
            "Epoch 85/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0357 - accuracy: 0.9815 - val_loss: 0.7256 - val_accuracy: 0.9167\n",
            "Epoch 86/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0310 - accuracy: 0.9907 - val_loss: 0.7230 - val_accuracy: 0.9167\n",
            "Epoch 87/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0274 - accuracy: 0.9907 - val_loss: 0.7568 - val_accuracy: 0.9167\n",
            "Epoch 88/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0286 - accuracy: 0.9907 - val_loss: 0.7583 - val_accuracy: 0.9167\n",
            "Epoch 89/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0260 - accuracy: 0.9907 - val_loss: 0.7269 - val_accuracy: 0.9167\n",
            "Epoch 90/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0321 - accuracy: 0.9907 - val_loss: 0.7549 - val_accuracy: 0.9167\n",
            "Epoch 91/100\n",
            "27/27 [==============================] - 0s 2ms/step - loss: 0.0281 - accuracy: 0.9907 - val_loss: 0.7543 - val_accuracy: 0.9167\n",
            "Epoch 92/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0275 - accuracy: 0.9907 - val_loss: 0.7246 - val_accuracy: 0.9167\n",
            "Epoch 93/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0268 - accuracy: 0.9907 - val_loss: 0.7358 - val_accuracy: 0.9167\n",
            "Epoch 94/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0250 - accuracy: 0.9907 - val_loss: 0.7458 - val_accuracy: 0.9167\n",
            "Epoch 95/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0360 - accuracy: 0.9907 - val_loss: 0.6975 - val_accuracy: 0.9167\n",
            "Epoch 96/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0350 - accuracy: 0.9815 - val_loss: 0.7614 - val_accuracy: 0.9167\n",
            "Epoch 97/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0255 - accuracy: 0.9907 - val_loss: 0.7671 - val_accuracy: 0.9167\n",
            "Epoch 98/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0262 - accuracy: 0.9907 - val_loss: 0.7652 - val_accuracy: 0.9167\n",
            "Epoch 99/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0276 - accuracy: 0.9907 - val_loss: 0.7462 - val_accuracy: 0.9167\n",
            "Epoch 100/100\n",
            "27/27 [==============================] - 0s 3ms/step - loss: 0.0301 - accuracy: 0.9815 - val_loss: 0.7207 - val_accuracy: 0.9167\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TM7lhNPOoxec",
        "outputId": "2a99edaa-469f-4565-f401-5ab10d6b6194"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 64)                320       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,675\n",
            "Trainable params: 4,675\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test_scaled, y_test_encoded)\n",
        "print(f\"Test Loss: {loss:.4f}\")\n",
        "print(f\"Test Accuracy: {accuracy:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7XpLGQ3Hon66",
        "outputId": "1de413e3-92b9-4ef9-d959-41aee4323bd1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 103ms/step - loss: 0.0081 - accuracy: 1.0000\n",
            "Test Loss: 0.0081\n",
            "Test Accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.title('Training Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "ywT3yZ7Vo-zK",
        "outputId": "804f4c28-6483-4035-a01e-1a18edcde5b0"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title(' Validation Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "JEeQbQIBpT_L",
        "outputId": "f54e4495-e467-4925-da77-c804262f1ffe"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, ' Validation Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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qpN/97ndnnLYNnGvoYQEAANUePSwAAKDaI7AAAIBqr0I9LDNmzNCvf/1r5efnq3379nrmmWfUuXPniGNPnTqlnJwczZ07V/v371fr1q315JNP6sYbb7TH5OTkaOnSpdq2bZuSkpLUtWtXPfnkk66eexEKhfTPf/5TtWvX5mmkAACcI4wxOnr0qBo3blzm6enfH+jKwoULTXx8vJk9e7b55JNPzNChQ03dunVNQUFBxPGPP/64ady4sVm+fLnZtWuXefbZZ01iYqLZvHmzPaZnz55mzpw5ZuvWrWbLli2mV69epmnTpubrr792fFz79u0zknjx4sWLFy9e5+Br37595V7nXTfdZmZm6sorr7SfZREKhZSWlqaf//znGjlyZJnxjRs31pgxYzRs2DB72x133KGkpCTNmzcv4nccPHhQjRo10ttvv61rrrnG0XEVFhaqbt262rdvn+rUqePmJwEAgCpSVFSktLQ0HTlyRMnJyWcc5+qW0MmTJ7Vp0yaNGjXK3ub3+9WjR48yD0eznDhxosyzPZKSkrRmzZozfk9hYaGkb584eiYnTpzQiRMn7D8fPXpUklSnTh0CCwAA55ho7Ryumm4PHTqkYDColJSUsO0pKSnKz8+P+JmePXsqNzdXO3bsUCgU0sqVK7V06VJ9+eWXEceHQiE98sgj6tatW8QHqVlycnKUnJxsv9LS0tz8FAAAcA6J+Syhp59+Wq1atVKbNm0UHx+v4cOHa8iQIWdsrBk2bJi2bt2qhQsXlrvfUaNGqbCw0H65eZQ7AAA4t7gKLA0aNFAgEFBBQUHY9oKCAqWmpkb8TMOGDbVs2TIdO3ZMX3zxhbZt26ZatWqVeWy6JA0fPlyvvfaa3nrrLV188cXlHktCQoJ9+4fbQAAAnN9cBZb4+Hh16tRJeXl59rZQKKS8vDx16dKl3M8mJiaqSZMmKikp0ZIlS3Trrbfa7xljNHz4cL3yyitatWqVmjdv7vJnAACA85nrdVhGjBih7OxsZWRkqHPnzpo+fbqOHTumIUOGSJIGDRqkJk2aKCcnR5K0bt067d+/Xx06dND+/fs1ceJEhUIhPf744/Y+hw0bpgULFugvf/mLateubffDJCcnKykpyYvfCQAAzmGuA8vdd9+tgwcPavz48crPz1eHDh20YsUKuxF37969Yf0pxcXFGjt2rHbv3q1atWqpV69eevHFF1W3bl17zHPPPSdJuvbaa8O+a86cORo8eLD7XwUAAM4r583DD4uKipScnKzCwkL6WQAAOEc4vX7zLCEAAFDtEVgAAEC1R2ABAADVHoEFAABUewQWAABQ7bme1vzv5qn/3a6ib07pwWsvUWpyYvQP6NuF8Oa+97m+OHw8xkcHAEDlGfGTS1U7sUaVfDeBJYqFG/bp4NET+tmVaY4Dyyf/LNLEv34a4yMDAKByPXhtSwJLdRX47nHXoZDzzxwtLpEk1atZQ/0zm8bisAAAqHQ146suNhBYogj4vw0sQRfr6wVD345NqZOox3q2iclxAQDw74Sm2yjswOKixGKFG/931RkAAHB2CCxRxNmBxflnrHATFyCwAADgBQJLFP7vAkuJmwrLd0OpsAAA4A0CSxRWhcVN061dYfETWAAA8AKBJQqrSlKhCguBBQAATxBYorD6UEIuZglZ4SbALSEAADxBYInCrrAEnQcWK9zQdAsAgDcILFHYPSxuKixBpjUDAOAlAksUp2cJVaDCQg8LAACeILBEcXodFjc9LN9VWAgsAAB4gsASRaACgSUUosICAICXCCxRWH0obgJLkAoLAACeIrBEcTa3hKiwAADgDQJLFP4KPK3ZarplHRYAALxBYIni9NL87issASosAAB4gsASRYWmNRNYAADwFIElirPpYSGwAADgDQJLFIEKzBKiwgIAgLcILFEEKtB0S4UFAABvEViisAOLi4cfBpklBACApwgsUVSkwmKFmwBPawYAwBMEligqsjQ/FRYAALxFYImiQoGFHhYAADxFYImiIrOECCwAAHiLwBKF1YdSocDCLSEAADxBYInCCh1uVrq1AwtNtwAAeILAEoX9LCE3s4SosAAA4CkCSxQVeZaQPUuIHhYAADxBYInCqpLwtGYAAKoOgSUKqw+lIk9rjiOwAADgCQJLFBWpsFg9LH4CCwAAniCwRFGhpfmpsAAA4CkCSxSBs2i69TNLCAAATxBYorCnNVfgllAc67AAAOAJAksUFZrWHKLCAgCAlwgsUVSkwlJi97BwegEA8AJX1Cj8FViaP2SvwxKTQwIA4N8Ol9QorD4UN0vzn144jtMLAIAXuKJGYVdYgi4qLIYKCwAAXuKSGoXVh+JmHRYr3FBhAQDAG1xRo7CqJEE3PSyGpzUDAOAlAksUVpXETWApsZfmj8khAQDwb4dLahQVqrAwrRkAAE9xRY3ibCosNN0CAOANLqlRWH0obgJLkGnNAAB4iitqFGfztGaabgEA8AaBJQo7sFTgac3WZwEAwNkhsERxNk23BBYAALxBYIni7JpuCSwAAHiBwBJFRZpuqbAAAOAtAksUFWm6LbHXYSGwAADgBQJLFGfTdOsnsAAA4AkCSxQVCixUWAAA8BSBJQq3gcUYY4/1sw4LAACeILBEEecysJQeRoUFAABvEFii8LsMLKXH0cMCAIA3CCxRxLmcJVQ6sFBhAQDAGwSWKPyl1mExDkJL6WDDOiwAAHiDwBJF6SqJk7tCwWCpW0I03QIA4AkCSxSl+1BKQqGo40tXWLglBACANwgsUYRVWKLnlbBQQ9MtAADeqFBgmTFjhtLT05WYmKjMzEytX7/+jGNPnTqlyZMnq2XLlkpMTFT79u21YsWKs9pnZQq4rLBYQ6iuAADgHdeBZdGiRRoxYoQmTJigzZs3q3379urZs6cOHDgQcfzYsWP1hz/8Qc8884w+/fRTPfDAA7rtttv0wQcfVHiflSlQwQoL1RUAALzjOrDk5uZq6NChGjJkiNq2bauZM2eqZs2amj17dsTxL774okaPHq1evXqpRYsWevDBB9WrVy899dRTFd5nZQr4qLAAAFDVXAWWkydPatOmTerRo8fpHfj96tGjh9auXRvxMydOnFBiYmLYtqSkJK1Zs6bC+7T2W1RUFPaKhdKVEidrsVihJsAMIQAAPOMqsBw6dEjBYFApKSlh21NSUpSfnx/xMz179lRubq527NihUCiklStXaunSpfryyy8rvE9JysnJUXJysv1KS0tz81NcsaolTm4Jhb4LNYEAgQUAAK/EfJbQ008/rVatWqlNmzaKj4/X8OHDNWTIEPn9Z/fVo0aNUmFhof3at2+fR0dcllVlcTSt+bshVFgAAPCOq9TQoEEDBQIBFRQUhG0vKChQampqxM80bNhQy5Yt07Fjx/TFF19o27ZtqlWrllq0aFHhfUpSQkKC6tSpE/aKFTcVFvuWED0sAAB4xlVgiY+PV6dOnZSXl2dvC4VCysvLU5cuXcr9bGJiopo0aaKSkhItWbJEt95661nvs7JY1RI3TbcEFgAAvBPn9gMjRoxQdna2MjIy1LlzZ02fPl3Hjh3TkCFDJEmDBg1SkyZNlJOTI0lat26d9u/frw4dOmj//v2aOHGiQqGQHn/8ccf7rGpWP0rITdMtgQUAAM+4Dix33323Dh48qPHjxys/P18dOnTQihUr7KbZvXv3hvWnFBcXa+zYsdq9e7dq1aqlXr166cUXX1TdunUd77Oqna6wRA8sdtMtgQUAAM/4jJNHEJ8DioqKlJycrMLCQs/7WTpP/ZsOHD2h5b+4Spc1Ti537Lrd/9Ldz7+vFg0v0KpfXuvpcQAAcL5xev3mWUIOWNWSoIMKi7VWC7OEAADwDoHFAVeBJcQtIQAAvEZgcYDAAgBA1SKwOFCRwMKzhAAA8A6BxQGrH8VNYOFpzQAAeIfA4oBdYXEwocq+JUTTLQAAniGwOBDwO1+HJcg6LAAAeI7A4sDpZwnRdAsAQFUgsDjgd1NhIbAAAOA5AosDVj+KkwpLCYEFAADPEVgccNN0G2JaMwAAniOwOFCRpfn9zBICAMAzBBYHKrRwXIDAAgCAVwgsDria1hyiwgIAgNcILA5UZFozPSwAAHiHwOKAVS1xVWEhsAAA4BkCiwNWP0rIwSyhEiosAAB4jsDigF1hCTqf1sw6LAAAeIfA4oDdw+KiwkJgAQDAOwQWB9wszW+FGp7WDACAdwgsDsS5WIfldIWFUwsAgFe4qjrgZuG40z0sMT0kAAD+rXBZdcBNYClhWjMAAJ4jsDhg9aO4WpqfwAIAgGcILA5Y/ShOntZsBRaabgEA8A6BxQGrH8XN05ppugUAwDtcVR3wu3lac5CmWwAAvMZl1QE305qpsAAA4D2uqg64abplWjMAAN7jsuqAm6ZbFo4DAMB7XFUdsJtuHTz80L4lxCQhAAA8Q2BxwNW0ZqvplntCAAB4hquqAxWa1sw6LAAAeIbA4oBdYWGlWwAAqgSBxQGrH8VNYOFZQgAAeIfA4oDVj0KFBQCAqkFgccDqRymhwgIAQJUgsDhgVUtCLh5+SIUFAADvEFgcsKoljios34UaP7OEAADwDIHFAbvC4iCwnF7plsACAIBXCCwOnK6whKKODXFLCAAAzxFYHDhdYYk+toSmWwAAPEdgccDvo8ICAEBVIrA4YIUPB88+tEMNTbcAAHiHwOJAwEXTrTUkjsc1AwDgGQKLA26mNVNhAQDAewQWB9xMa7baXOhhAQDAOwQWB9w03QZZhwUAAM8RWByw+lEcFFhYOA4AgBggsDjgalqzIbAAAOA1AosDrhaOC347iMACAIB3CCwOBNwszf/dbaMAs4QAAPAMgcUBK7AEHS3NT4UFAACvEVgcOB1YnCzNH/4ZAABw9ggsDpwOLM4XjmMdFgAAvENgccDqR4kWWIwxdg8LT2sGAMA7BBYH7AqLKT+wlA40VFgAAPAOgcUBp7eESgcaKiwAAHiHwOJAnNPAUup9pjUDAOAdAosDVrUkZL7tUzmTsMBChQUAAM8QWBwo3Y9SXpWFwAIAQGwQWBwo3Y9SXuMtt4QAAIgNAosDpcOHkwqLz0fTLQAAXiKwOBBwekvou+oLU5oBAPAWgcUBx4Hlu/f83A4CAMBTBBYH3N4SosICAIC3CCwO+P0+WZnFUYWFwAIAgKcILA7FOVienwoLAACxQWBxyOpLKQlGb7plDRYAALxVocAyY8YMpaenKzExUZmZmVq/fn2546dPn67WrVsrKSlJaWlpevTRR1VcXGy/HwwGNW7cODVv3lxJSUlq2bKlpkyZUu6qspUtzl7t9szHZIUZAgsAAN6Kc/uBRYsWacSIEZo5c6YyMzM1ffp09ezZU9u3b1ejRo3KjF+wYIFGjhyp2bNnq2vXrvrss880ePBg+Xw+5ebmSpKefPJJPffcc5o7d64uu+wybdy4UUOGDFFycrJ+8YtfnP2v9IDVl1JSTg+LFWZYNA4AAG+5rrDk5uZq6NChGjJkiNq2bauZM2eqZs2amj17dsTx7733nrp166b+/fsrPT1dN9xwg/r16xdWlXnvvfd06623qnfv3kpPT9edd96pG264IWrlpjLZFZZyAosVZgIBAgsAAF5yFVhOnjypTZs2qUePHqd34PerR48eWrt2bcTPdO3aVZs2bbLDx+7du/X666+rV69eYWPy8vL02WefSZI+/PBDrVmzRjfddNMZj+XEiRMqKioKe8VSwEmFJUSFBQCAWHB1S+jQoUMKBoNKSUkJ256SkqJt27ZF/Ez//v116NAhXXXVVTLGqKSkRA888IBGjx5tjxk5cqSKiorUpk0bBQIBBYNBTZ06VVlZWWc8lpycHE2aNMnN4Z8VK7CUN63ZrrDQwwIAgKdiPkto9erVmjZtmp599llt3rxZS5cu1fLlyzVlyhR7zJ///GfNnz9fCxYs0ObNmzV37lz95je/0dy5c8+431GjRqmwsNB+7du3L6a/w6qalBdYQgQWAABiwlWFpUGDBgoEAiooKAjbXlBQoNTU1IifGTdunAYOHKh7771XktSuXTsdO3ZM9913n8aMGSO/36/HHntMI0eOVN++fe0xX3zxhXJycpSdnR1xvwkJCUpISHBz+GfF6kspbx2WEpbmBwAgJlxVWOLj49WpUyfl5eXZ20KhkPLy8tSlS5eInzl+/Lj8/vCvCQQCkmRPWz7TmFAo5ObwYsqqsJTXdGs//JCmWwAAPOV6WvOIESOUnZ2tjIwMde7cWdOnT9exY8c0ZMgQSdKgQYPUpEkT5eTkSJL69Omj3NxcdezYUZmZmdq5c6fGjRunPn362MGlT58+mjp1qpo2barLLrtMH3zwgXJzc3XPPfd4+FPPjpOm22CQplsAAGLBdWC5++67dfDgQY0fP175+fnq0KGDVqxYYTfi7t27N6xaMnbsWPl8Po0dO1b79+9Xw4YN7YBieeaZZzRu3Dg99NBDOnDggBo3bqz7779f48eP9+AneiPgYFozK90CABAbPlOdlpM9C0VFRUpOTlZhYaHq1Knj+f5vnP6OtuUf1Z/u6axrLm0YcczrH3+ph+Zv1pXp9bT4ga6eHwMAAOcbp9dvniXkUJyDptsgs4QAAIgJAotD9rTm8h5+SGABACAmCCwO2QvHOaqwcFoBAPASV1aHnKx0azfdUmABAMBTBBaHHAUWKiwAAMQEV1aH3AWWSjkkAAD+bXBpdciqmjgJLHFUWAAA8BRXVoesvhQngcXPLCEAADxFYHHIrrA4mCUUR2ABAMBTBBaHrL6Ucp8lZHhaMwAAsUBgccjqSyn3WUJUWAAAiAkCi0N+J09rpocFAICYILA4FOfgac0lVFgAAIgJAotDVl9KeU23IZ4lBABATBBYHIpzsHCcVWGh6RYAAG8RWBzyOwgsoe+qL3E8TAgAAE8RWBxyMq25JEiFBQCAWCCwOORkWrNdYaGHBQAATxFYHLKqJuVWWEKhb8cSWAAA8BSBxSGrLyVU7tL8340lsAAA4CkCi0N2hSXItGYAACobgcUhe+G4ciosJQQWAABigsDi0Oml+UNnHGOFmQCzhAAA8BSBxaHTC8edeQwVFgAAYoPA4lDADizlVFgILAAAxASBxaGAowpLKGwsAADwBoHFIasvpbwKixVmCCwAAHiLwOKQXWE58yQhO8wQWAAA8BaBxSEnPSxWmGGWEAAA3iKwOBRw8LRmK8zwtGYAALxFYHHISdOtFWZ4WjMAAN4isDjk6JYQ05oBAIgJAotD9iyhcptuCSwAAMQCgcUhqy/FUYWFW0IAAHiKwOKQ3+eg6dZ6lhBNtwAAeIrA4pCTWUIlQSosAADEAoHFISeBxXpacxw9LAAAeIrA4lDAyS0ha1ozgQUAAE8RWByy+lKsPpVIrMBChQUAAG8RWByyKiwl5cxrtsIMFRYAALxFYHHIqpqEyquwBKmwAAAQCwQWh6yqSYmDac0szQ8AgLcILA7ZFRYHTbc8/BAAAG8RWBxyVGFhpVsAAGKCwOKQkwpLCc8SAgAgJggsDll9KeVVWEIEFgAAYoLA4pDVl1LeLCEqLAAAxAaBxSEnK91aYYbAAgCAtwgsDgUcNN2W0HQLAEBMEFgcCkRpug2FjKy7RVRYAADwFoHFoWgVltLPGCKwAADgLQKLQ4EoS/OX7m0hsAAA4C0Ci0OBKNOaCSwAAMQOgcUhK4QYE7mPhVtCAADEDoHFodIhJBjhtpD1pGaJWUIAAHiNwOJQWGChwgIAQKUisDgULbBYt4n8PslHhQUAAE8RWByKdkvIasaN83NKAQDwGldXh0r3pZTuV7G3WRUWzigAAJ7j8upQ1KZbKiwAAMQMV1eHfD6frMxSXtMt/bYAAHiPwOKCVT2JGFisCkuAUwoAgNe4urpg3e0pL7D4mSEEAIDnCCwuOKqwcE8IAADPEVhcsHtYymm6ZdE4AAC8R2BxwepPiVRhKSGwAAAQMwQWF6z+lIgr3RoCCwAAsUJgccHqT4lYYQkyrRkAgFghsLgQKCewWBUWFo4DAMB7XF1dsLJISTk9LH5KLAAAeK5CgWXGjBlKT09XYmKiMjMztX79+nLHT58+Xa1bt1ZSUpLS0tL06KOPqri4OGzM/v37NWDAANWvX19JSUlq166dNm7cWJHDixmrehKKMEsoxLRmAABiJs7tBxYtWqQRI0Zo5syZyszM1PTp09WzZ09t375djRo1KjN+wYIFGjlypGbPnq2uXbvqs88+0+DBg+Xz+ZSbmytJ+uqrr9StWzddd911euONN9SwYUPt2LFD9erVO/tf6CEri5REePghFRYAAGLHdWDJzc3V0KFDNWTIEEnSzJkztXz5cs2ePVsjR44sM/69995Tt27d1L9/f0lSenq6+vXrp3Xr1tljnnzySaWlpWnOnDn2tubNm7v+MbFWXoWFheMAAIgdV7eETp48qU2bNqlHjx6nd+D3q0ePHlq7dm3Ez3Tt2lWbNm2ybxvt3r1br7/+unr16mWPefXVV5WRkaG77rpLjRo1UseOHfXCCy+UeywnTpxQUVFR2CvWrOpJpB4We1ozS/MDAOA5V4Hl0KFDCgaDSklJCduekpKi/Pz8iJ/p37+/Jk+erKuuuko1atRQy5Ytde2112r06NH2mN27d+u5555Tq1at9Oabb+rBBx/UL37xC82dO/eMx5KTk6Pk5GT7lZaW5uanVIhVPQmxcBwAAJUq5rOEVq9erWnTpunZZ5/V5s2btXTpUi1fvlxTpkyxx4RCIV1xxRWaNm2aOnbsqPvuu09Dhw7VzJkzz7jfUaNGqbCw0H7t27cv1j+l/AoLgQUAgJhx1cPSoEEDBQIBFRQUhG0vKChQampqxM+MGzdOAwcO1L333itJateunY4dO6b77rtPY8aMkd/v10UXXaS2bduGfe4HP/iBlixZcsZjSUhIUEJCgpvDP2vlLhxHYAEAIGZcVVji4+PVqVMn5eXl2dtCoZDy8vLUpUuXiJ85fvy4/N9bTC0QCEiSzHd9H926ddP27dvDxnz22Wdq1qyZm8OLuUB5S/MTWAAAiBnXs4RGjBih7OxsZWRkqHPnzpo+fbqOHTtmzxoaNGiQmjRpopycHElSnz59lJubq44dOyozM1M7d+7UuHHj1KdPHzu4PProo+rataumTZumn/3sZ1q/fr2ef/55Pf/88x7+1LNnr3QbYZYQFRYAAGLHdWC5++67dfDgQY0fP175+fnq0KGDVqxYYTfi7t27N6yiMnbsWPl8Po0dO1b79+9Xw4YN1adPH02dOtUec+WVV+qVV17RqFGjNHnyZDVv3lzTp09XVlaWBz/RO6eX5g+VeS/ILCEAAGLGZ0yEcsE5qKioSMnJySosLFSdOnVi8h3Zs9fr7c8O6jd3tdednS4Oe++Pf9+jiX/9VL1/eJFm9L8iJt8PAMD5xun1m2cJuRAoZ1qztfgtFRYAALxHYHEhUM60Zus2ESvdAgDgPQKLC/YsoYhL83/7T54lBACA9wgsLgQC3wWWYISm2+8qLNwSAgDAewQWF05XWMq+Z2UYK9QAAADvEFhcKHdaMxUWAABihsDiwunAUvY9ex0WelgAAPAcgcWF00vzl00srHQLAEDsEFhcsJtuI1RYrLVZmNYMAID3CCwuOKmwMK0ZAADvEVhcKO/hh1RYAACIHQKLC+WudPtdiPEzSwgAAM8RWFyIK+9ZQlRYAACIGQKLC/5ynyVEDwsAALFCYHGhvApLCRUWAABihsDigr+chx+GWIcFAICYIbC4EGevdHvmCguBBQAA7xFYXPCXE1hCLM0PAEDMEFhciCun6bYkSGABACBWCCwuBMppurUrLKzDAgCA5wgsLpS3cBw9LAAAxA6BxQW7whJhllCQwAIAQMwQWFywpjVb/SqlEVgAAIgdAosLcVRYAACoEgQWF5wszU/TLQAA3iOwuFDewnFB1mEBACBmCCwuBMoLLNwSAgAgZggsLhBYAACoGgQWF6z+FAILAACVi8Digl1hYZYQAACVisDiQrm3hFiaHwCAmCGwuOCkhyUuQGABAMBrBBYXnAQWPxUWAAA8R2BxwVGFxc8pBQDAa1xdXbBnCZXTdEteAQDAe1xeXbD6U6iwAABQubi6uuAvbx0We2n+Sj0kAAD+LXB5dcGqnkQMLEErsHBKAQDwGldXF6wswjosAABULgKLC+VVWEpougUAIGa4vLpg9adEmiUUoukWAICY4erqgt10G6TCAgBAZeLy6oJ9S+h7FZZQqVtEVFgAAPAeV1cXrCxS8r0eltJ/pukWAADvEVhcsKonoe8FllCpikuAhx8CAOA5AosLZ6qwBKmwAAAQUwQWF0r3p5SusoTdEvITWAAA8BqBxYXS1ZPSISVEYAEAIKYILC6U7k8p3bdSOryQVwAA8B6BxYXSFZbSfSsh+8GHPvnoYQEAwHMEFhdK3+4pidDDwu0gAABig8DiQulAEorQw8IMIQAAYoPA4kLpAkqkCkscFRYAAGKCwOKCz+ezqyylm26D9nOECCwAAMQCgcUlK7CUrrAEqbAAABBTBBaXrD6VUITAQoUFAIDYILC4FFdOhYWmWwAAYoPA4pJVRSm9DkvQMK0ZAIBYIrC4FIgUWEKhsPcAAIC3CCwuRQ4s3/6TplsAAGKDwOKS1acSDFuH5dvEQtMtAACxQWBxya6wmNIr3X77TyosAADEBoHFpdO3hEL2NrvCwiwhAABigsDiUpwdWE5vs1a9jQsQWAAAiAUCi0t+ex2W04nFCi9UWAAAiA0Ci0tWhaVUXrFvD9HDAgBAbBBYXLKqKEFTdlozs4QAAIgNAotLVp9KpKZbKiwAAMQGgcUlu8ISoemWlW4BAIgNAotLcZGmNQcJLAAAxFKFAsuMGTOUnp6uxMREZWZmav369eWOnz59ulq3bq2kpCSlpaXp0UcfVXFxccSxTzzxhHw+nx555JGKHFrM+cuZ1szTmgEAiA3XgWXRokUaMWKEJkyYoM2bN6t9+/bq2bOnDhw4EHH8ggULNHLkSE2YMEH/+Mc/NGvWLC1atEijR48uM3bDhg36wx/+oB/+8Ifuf0kliYswrbkkRIUFAIBYch1YcnNzNXToUA0ZMkRt27bVzJkzVbNmTc2ePTvi+Pfee0/dunVT//79lZ6erhtuuEH9+vUrU5X5+uuvlZWVpRdeeEH16tWr2K+pBFYoCYUtzU9gAQAgllwFlpMnT2rTpk3q0aPH6R34/erRo4fWrl0b8TNdu3bVpk2b7ICye/duvf766+rVq1fYuGHDhql3795h+y7PiRMnVFRUFPaqDFYosfpWJCosAADEWpybwYcOHVIwGFRKSkrY9pSUFG3bti3iZ/r3769Dhw7pqquukjFGJSUleuCBB8JuCS1cuFCbN2/Whg0bHB9LTk6OJk2a5ObwPWH1qYTC1mEhsAAAEEsxnyW0evVqTZs2Tc8++6w2b96spUuXavny5ZoyZYokad++fXr44Yc1f/58JSYmOt7vqFGjVFhYaL/27dsXq58Q5vTS/BECC023AADEhKsKS4MGDRQIBFRQUBC2vaCgQKmpqRE/M27cOA0cOFD33nuvJKldu3Y6duyY7rvvPo0ZM0abNm3SgQMHdMUVV9ifCQaDeuedd/T73/9eJ06cUCAQKLPfhIQEJSQkuDl8T5xemr9UYGEdFgAAYspVhSU+Pl6dOnVSXl6evS0UCikvL09dunSJ+Jnjx4/L7w//GiuAGGP04x//WB9//LG2bNlivzIyMpSVlaUtW7ZEDCtVKWKFhXVYAACIKVcVFkkaMWKEsrOzlZGRoc6dO2v69Ok6duyYhgwZIkkaNGiQmjRpopycHElSnz59lJubq44dOyozM1M7d+7UuHHj1KdPHwUCAdWuXVuXX3552HdccMEFql+/fpnt1cHpheOosAAAUFlcB5a7775bBw8e1Pjx45Wfn68OHTpoxYoVdiPu3r17wyoqY8eOlc/n09ixY7V//341bNhQffr00dSpU737FZUo4IsQWGi6BQAgplwHFkkaPny4hg8fHvG91atXh39BXJwmTJigCRMmON7/9/dRnVihJMgsIQAAKg3PEnLJDizBCLeEmCUEAEBMEFhcilhhsZpuAwQWAABigcDiUqC8ac1UWAAAiAkCi0uBchaOi6OHBQCAmCCwuGTPEorQdOsnsAAAEBMEFpesPpWwplsqLAAAxBSBxSUqLAAAVD4Ci0sRV7qlwgIAQEwRWFzyl7M0v59ZQgAAxASBxaVIFZYSKiwAAMQUgcWlSBWWEEvzAwAQUwQWlyI9/LCEplsAAGKKwOJSpKX5Q9wSAgAgpggsLkVa6dausNB0CwBATBBYXIqL8Cyh0HfVljgefggAQEwQWFzyR6qwBKmwAAAQSwQWlyJVWKx+ljg/pxMAgFjgCuuSVUWJ9LTmAGcTAICYiKvqAzjXWH0qOw98rUl//USStPfwcUlSgAoLAAAxQWBxKTmphiRp/5FvNOfvn4e9VyeR0wkAQCxwhXXp6lYNNemWy3TgaHHY9ouSk3Rl+oVVdFQAAJzfCCwu1Qj4ld01vaoPAwCAfys0XQAAgGqPwAIAAKo9AgsAAKj2CCwAAKDaI7AAAIBqj8ACAACqPQILAACo9ggsAACg2iOwAACAao/AAgAAqj0CCwAAqPYILAAAoNojsAAAgGrvvHlaszFGklRUVFTFRwIAAJyyrtvWdfxMzpvAcvToUUlSWlpaFR8JAABw6+jRo0pOTj7j+z4TLdKcI0KhkP75z3+qdu3a8vl8nu23qKhIaWlp2rdvn+rUqePZflEW57rycK4rD+e6cnG+K49X59oYo6NHj6px48by+8/cqXLeVFj8fr8uvvjimO2/Tp06/B9/JeFcVx7OdeXhXFcuznfl8eJcl1dZsdB0CwAAqj0CCwAAqPYILFEkJCRowoQJSkhIqOpDOe9xrisP57rycK4rF+e78lT2uT5vmm4BAMD5iwoLAACo9ggsAACg2iOwAACAao/AAgAAqj0CCwAAqPYILFHMmDFD6enpSkxMVGZmptavX1/Vh3ROy8nJ0ZVXXqnatWurUaNG+ulPf6rt27eHjSkuLtawYcNUv3591apVS3fccYcKCgqq6IjPH0888YR8Pp8eeeQRexvn2lv79+/XgAEDVL9+fSUlJaldu3bauHGj/b4xRuPHj9dFF12kpKQk9ejRQzt27KjCIz43BYNBjRs3Ts2bN1dSUpJatmypKVOmhD08j3NdMe+884769Omjxo0by+fzadmyZWHvOzmvhw8fVlZWlurUqaO6devqP/7jP/T111+f/cEZnNHChQtNfHy8mT17tvnkk0/M0KFDTd26dU1BQUFVH9o5q2fPnmbOnDlm69atZsuWLaZXr16madOm5uuvv7bHPPDAAyYtLc3k5eWZjRs3mh/96Eema9euVXjU577169eb9PR088Mf/tA8/PDD9nbOtXcOHz5smjVrZgYPHmzWrVtndu/ebd58802zc+dOe8wTTzxhkpOTzbJly8yHH35obrnlFtO8eXPzzTffVOGRn3umTp1q6tevb1577TWzZ88es3jxYlOrVi3z9NNP22M41xXz+uuvmzFjxpilS5caSeaVV14Je9/Jeb3xxhtN+/btzfvvv2/effddc8kll5h+/fqd9bERWMrRuXNnM2zYMPvPwWDQNG7c2OTk5FThUZ1fDhw4YCSZt99+2xhjzJEjR0yNGjXM4sWL7TH/+Mc/jCSzdu3aqjrMc9rRo0dNq1atzMqVK0337t3twMK59tZ//ud/mquuuuqM74dCIZOammp+/etf29uOHDliEhISzEsvvVQZh3je6N27t7nnnnvCtt1+++0mKyvLGMO59sr3A4uT8/rpp58aSWbDhg32mDfeeMP4fD6zf//+szoebgmdwcmTJ7Vp0yb16NHD3ub3+9WjRw+tXbu2Co/s/FJYWChJuvDCCyVJmzZt0qlTp8LOe5s2bdS0aVPOewUNGzZMvXv3DjunEufaa6+++qoyMjJ01113qVGjRurYsaNeeOEF+/09e/YoPz8/7HwnJycrMzOT8+1S165dlZeXp88++0yS9OGHH2rNmjW66aabJHGuY8XJeV27dq3q1q2rjIwMe0yPHj3k9/u1bt26s/r+8+ZpzV47dOiQgsGgUlJSwranpKRo27ZtVXRU55dQKKRHHnlE3bp10+WXXy5Jys/PV3x8vOrWrRs2NiUlRfn5+VVwlOe2hQsXavPmzdqwYUOZ9zjX3tq9e7eee+45jRgxQqNHj9aGDRv0i1/8QvHx8crOzrbPaaR/p3C+3Rk5cqSKiorUpk0bBQIBBYNBTZ06VVlZWZLEuY4RJ+c1Pz9fjRo1Cns/Li5OF1544VmfewILqsywYcO0detWrVmzpqoP5by0b98+Pfzww1q5cqUSExOr+nDOe6FQSBkZGZo2bZokqWPHjtq6datmzpyp7OzsKj6688uf//xnzZ8/XwsWLNBll12mLVu26JFHHlHjxo051+cxbgmdQYMGDRQIBMrMmCgoKFBqamoVHdX5Y/jw4Xrttdf01ltv6eKLL7a3p6am6uTJkzpy5EjYeM67e5s2bdKBAwd0xRVXKC4uTnFxcXr77bf1u9/9TnFxcUpJSeFce+iiiy5S27Ztw7b94Ac/0N69eyXJPqf8O+XsPfbYYxo5cqT69u2rdu3aaeDAgXr00UeVk5MjiXMdK07Oa2pqqg4cOBD2fklJiQ4fPnzW557Acgbx8fHq1KmT8vLy7G2hUEh5eXnq0qVLFR7Zuc0Yo+HDh+uVV17RqlWr1Lx587D3O3XqpBo1aoSd9+3bt2vv3r2cd5d+/OMf6+OPP9aWLVvsV0ZGhrKysuz/zbn2Trdu3cpM0f/ss8/UrFkzSVLz5s2Vmpoadr6Lioq0bt06zrdLx48fl98ffvkKBAIKhUKSONex4uS8dunSRUeOHNGmTZvsMatWrVIoFFJmZubZHcBZteye5xYuXGgSEhLMH//4R/Ppp5+a++67z9StW9fk5+dX9aGdsx588EGTnJxsVq9ebb788kv7dfz4cXvMAw88YJo2bWpWrVplNm7caLp06WK6dOlShUd9/ig9S8gYzrWX1q9fb+Li4szUqVPNjh07zPz5803NmjXNvHnz7DFPPPGEqVu3rvnLX/5iPvroI3Prrbcy1bYCsrOzTZMmTexpzUuXLjUNGjQwjz/+uD2Gc10xR48eNR988IH54IMPjCSTm5trPvjgA/PFF18YY5yd1xtvvNF07NjRrFu3zqxZs8a0atWKac2V4ZlnnjFNmzY18fHxpnPnzub999+v6kM6p0mK+JozZ4495ptvvjEPPfSQqVevnqlZs6a57bbbzJdffll1B30e+X5g4Vx7669//au5/PLLTUJCgmnTpo15/vnnw94PhUJm3LhxJiUlxSQkJJgf//jHZvv27VV0tOeuoqIi8/DDD5umTZuaxMRE06JFCzNmzBhz4sQJewznumLeeuutiP+Ozs7ONsY4O6//+te/TL9+/UytWrVMnTp1zJAhQ8zRo0fP+th8xpRaGhAAAKAaoocFAABUewQWAABQ7RFYAABAtUdgAQAA1R6BBQAAVHsEFgAAUO0RWAAAQLVHYAEAANUegQUAAFR7BBYAAFDtEVgAAEC19/+hGXa+VLrqQQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}